Overview

Dataset statistics

Number of variables12
Number of observations51053
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical2

Reproduction

Analysis started2023-03-12 10:31:54.403442
Analysis finished2023-03-12 10:32:14.531303
Duration20.13 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

t1_champ1win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.242187
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:14.648579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1779306
Coefficient of variation (CV)0.02344505
Kurtosis-0.014731447
Mean50.242187
Median Absolute Deviation (MAD)0.80666667
Skewness0.090016844
Sum2565014.4
Variance1.3875205
MonotonicityNot monotonic
2023-03-12T10:32:14.807950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1599
 
3.1%
49.26 1284
 
2.5%
49.56 1268
 
2.5%
48.88 1170
 
2.3%
50.52 1061
 
2.1%
51.63 1020
 
2.0%
49.95 996
 
2.0%
51.04 975
 
1.9%
48.45 900
 
1.8%
50.83 870
 
1.7%
Other values (115) 39910
78.2%
ValueCountFrequency (%)
45.94 111
 
0.2%
46.67 146
 
0.3%
47.265 175
 
0.3%
47.67 124
 
0.2%
47.935 221
 
0.4%
48.05 154
 
0.3%
48.09 358
0.7%
48.13 605
1.2%
48.165 451
0.9%
48.365 63
 
0.1%
ValueCountFrequency (%)
53.01 231
 
0.5%
52.85 464
0.9%
52.815 180
 
0.4%
52.79 300
0.6%
52.75 185
 
0.4%
52.74 301
0.6%
52.51 136
 
0.3%
52.45 317
0.6%
52.31666667 97
 
0.2%
52.28 613
1.2%

t2_champ1win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.250693
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:14.965537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1791696
Coefficient of variation (CV)0.023465737
Kurtosis-0.048930483
Mean50.250693
Median Absolute Deviation (MAD)0.81
Skewness0.10259465
Sum2565448.6
Variance1.3904409
MonotonicityNot monotonic
2023-03-12T10:32:15.137443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1689
 
3.3%
49.56 1341
 
2.6%
49.26 1253
 
2.5%
48.88 1240
 
2.4%
50.52 1152
 
2.3%
51.63 1064
 
2.1%
51.04 964
 
1.9%
49.95 957
 
1.9%
50.83 892
 
1.7%
48.45 888
 
1.7%
Other values (115) 39613
77.6%
ValueCountFrequency (%)
45.94 114
 
0.2%
46.67 120
 
0.2%
47.265 141
 
0.3%
47.67 113
 
0.2%
47.935 202
 
0.4%
48.05 163
 
0.3%
48.09 364
0.7%
48.13 653
1.3%
48.165 443
0.9%
48.365 62
 
0.1%
ValueCountFrequency (%)
53.01 225
 
0.4%
52.85 515
1.0%
52.815 198
 
0.4%
52.79 296
0.6%
52.75 183
 
0.4%
52.74 321
0.6%
52.51 154
 
0.3%
52.45 325
0.6%
52.31666667 111
 
0.2%
52.28 601
1.2%

t1_champ2win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.258421
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:15.293785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.46
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.58

Descriptive statistics

Standard deviation1.1825489
Coefficient of variation (CV)0.023529368
Kurtosis-0.057877135
Mean50.258421
Median Absolute Deviation (MAD)0.81
Skewness0.090929228
Sum2565843.2
Variance1.3984219
MonotonicityNot monotonic
2023-03-12T10:32:15.445128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1728
 
3.4%
48.88 1330
 
2.6%
49.26 1286
 
2.5%
50.52 1257
 
2.5%
49.56 1192
 
2.3%
51.63 1072
 
2.1%
51.04 998
 
2.0%
49.95 985
 
1.9%
52.14 910
 
1.8%
48.45 895
 
1.8%
Other values (115) 39400
77.2%
ValueCountFrequency (%)
45.94 116
 
0.2%
46.67 111
 
0.2%
47.265 153
 
0.3%
47.67 128
 
0.3%
47.935 218
 
0.4%
48.05 156
 
0.3%
48.09 429
0.8%
48.13 636
1.2%
48.165 398
0.8%
48.365 59
 
0.1%
ValueCountFrequency (%)
53.01 233
 
0.5%
52.85 528
1.0%
52.815 186
 
0.4%
52.79 318
0.6%
52.75 173
 
0.3%
52.74 296
0.6%
52.51 154
 
0.3%
52.45 348
0.7%
52.31666667 93
 
0.2%
52.28 607
1.2%

t2_champ2win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.260068
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:15.604167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1879022
Coefficient of variation (CV)0.023635108
Kurtosis-0.12684481
Mean50.260068
Median Absolute Deviation (MAD)0.81
Skewness0.11039812
Sum2565927.3
Variance1.4111115
MonotonicityNot monotonic
2023-03-12T10:32:15.755552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1754
 
3.4%
48.88 1324
 
2.6%
49.26 1322
 
2.6%
50.52 1218
 
2.4%
49.56 1213
 
2.4%
51.63 1105
 
2.2%
51.04 986
 
1.9%
49.95 982
 
1.9%
48.45 950
 
1.9%
52.14 912
 
1.8%
Other values (115) 39287
77.0%
ValueCountFrequency (%)
45.94 85
 
0.2%
46.67 136
 
0.3%
47.265 168
 
0.3%
47.67 115
 
0.2%
47.935 205
 
0.4%
48.05 172
 
0.3%
48.09 436
0.9%
48.13 622
1.2%
48.165 403
0.8%
48.365 59
 
0.1%
ValueCountFrequency (%)
53.01 221
 
0.4%
52.85 592
1.2%
52.815 188
 
0.4%
52.79 342
0.7%
52.75 165
 
0.3%
52.74 332
0.7%
52.51 128
 
0.3%
52.45 328
0.6%
52.31666667 92
 
0.2%
52.28 607
1.2%

t1_champ3win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.271984
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:15.925964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.46
median50.1
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.58

Descriptive statistics

Standard deviation1.1809347
Coefficient of variation (CV)0.02349091
Kurtosis-0.099333116
Mean50.271984
Median Absolute Deviation (MAD)0.82
Skewness0.10397759
Sum2566535.6
Variance1.3946067
MonotonicityNot monotonic
2023-03-12T10:32:16.091739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1635
 
3.2%
48.88 1307
 
2.6%
49.26 1282
 
2.5%
50.52 1234
 
2.4%
49.56 1177
 
2.3%
51.63 1073
 
2.1%
51.04 1002
 
2.0%
49.95 968
 
1.9%
50.56 963
 
1.9%
48.45 954
 
1.9%
Other values (115) 39458
77.3%
ValueCountFrequency (%)
45.94 97
 
0.2%
46.67 115
 
0.2%
47.265 149
 
0.3%
47.67 115
 
0.2%
47.935 205
 
0.4%
48.05 154
 
0.3%
48.09 385
0.8%
48.13 609
1.2%
48.165 411
0.8%
48.365 48
 
0.1%
ValueCountFrequency (%)
53.01 259
0.5%
52.85 540
1.1%
52.815 178
 
0.3%
52.79 375
0.7%
52.75 166
 
0.3%
52.74 295
0.6%
52.51 156
 
0.3%
52.45 310
0.6%
52.31666667 95
 
0.2%
52.28 620
1.2%

t2_champ3win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.261409
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:16.253448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1878377
Coefficient of variation (CV)0.023633195
Kurtosis-0.10828116
Mean50.261409
Median Absolute Deviation (MAD)0.81
Skewness0.10603959
Sum2565995.7
Variance1.4109584
MonotonicityNot monotonic
2023-03-12T10:32:16.407277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1720
 
3.4%
48.88 1328
 
2.6%
49.26 1306
 
2.6%
50.52 1228
 
2.4%
49.56 1181
 
2.3%
51.63 1031
 
2.0%
51.04 994
 
1.9%
49.95 924
 
1.8%
48.45 910
 
1.8%
52.14 891
 
1.7%
Other values (115) 39540
77.4%
ValueCountFrequency (%)
45.94 102
 
0.2%
46.67 120
 
0.2%
47.265 144
 
0.3%
47.67 117
 
0.2%
47.935 233
 
0.5%
48.05 160
 
0.3%
48.09 393
0.8%
48.13 646
1.3%
48.165 433
0.8%
48.365 58
 
0.1%
ValueCountFrequency (%)
53.01 241
 
0.5%
52.85 576
1.1%
52.815 202
 
0.4%
52.79 331
0.6%
52.75 153
 
0.3%
52.74 326
0.6%
52.51 152
 
0.3%
52.45 308
0.6%
52.31666667 116
 
0.2%
52.28 633
1.2%

t1_champ4win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.25691
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:16.564642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1850515
Coefficient of variation (CV)0.023579871
Kurtosis-0.12829556
Mean50.25691
Median Absolute Deviation (MAD)0.81
Skewness0.10727899
Sum2565766
Variance1.404347
MonotonicityNot monotonic
2023-03-12T10:32:16.761599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1707
 
3.3%
49.26 1325
 
2.6%
48.88 1322
 
2.6%
50.52 1244
 
2.4%
49.56 1184
 
2.3%
51.63 1042
 
2.0%
48.45 957
 
1.9%
51.04 956
 
1.9%
49.95 924
 
1.8%
50.56 907
 
1.8%
Other values (115) 39485
77.3%
ValueCountFrequency (%)
45.94 97
 
0.2%
46.67 123
 
0.2%
47.265 137
 
0.3%
47.67 100
 
0.2%
47.935 211
 
0.4%
48.05 185
 
0.4%
48.09 429
0.8%
48.13 655
1.3%
48.165 409
0.8%
48.365 65
 
0.1%
ValueCountFrequency (%)
53.01 207
 
0.4%
52.85 544
1.1%
52.815 173
 
0.3%
52.79 334
0.7%
52.75 160
 
0.3%
52.74 338
0.7%
52.51 145
 
0.3%
52.45 345
0.7%
52.31666667 86
 
0.2%
52.28 611
1.2%

t2_champ4win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.262191
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:17.032317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.1
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1811946
Coefficient of variation (CV)0.023500659
Kurtosis-0.11455734
Mean50.262191
Median Absolute Deviation (MAD)0.82
Skewness0.10179565
Sum2566035.6
Variance1.3952207
MonotonicityNot monotonic
2023-03-12T10:32:17.300516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1805
 
3.5%
49.26 1339
 
2.6%
48.88 1290
 
2.5%
49.56 1253
 
2.5%
50.52 1248
 
2.4%
51.63 1080
 
2.1%
51.04 1013
 
2.0%
49.95 956
 
1.9%
48.45 860
 
1.7%
49.26 853
 
1.7%
Other values (115) 39356
77.1%
ValueCountFrequency (%)
45.94 91
 
0.2%
46.67 117
 
0.2%
47.265 165
 
0.3%
47.67 135
 
0.3%
47.935 199
 
0.4%
48.05 170
 
0.3%
48.09 385
0.8%
48.13 680
1.3%
48.165 392
0.8%
48.365 57
 
0.1%
ValueCountFrequency (%)
53.01 214
 
0.4%
52.85 541
1.1%
52.815 199
 
0.4%
52.79 340
0.7%
52.75 193
 
0.4%
52.74 269
0.5%
52.51 131
 
0.3%
52.45 348
0.7%
52.31666667 109
 
0.2%
52.28 604
1.2%

t1_champ5win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.246449
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:17.559463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.186211
Coefficient of variation (CV)0.023607858
Kurtosis-0.056189517
Mean50.246449
Median Absolute Deviation (MAD)0.81
Skewness0.1038327
Sum2565232
Variance1.4070966
MonotonicityNot monotonic
2023-03-12T10:32:17.825070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1604
 
3.1%
48.88 1273
 
2.5%
49.26 1251
 
2.5%
49.56 1242
 
2.4%
50.52 1079
 
2.1%
51.63 1052
 
2.1%
49.95 1004
 
2.0%
51.04 975
 
1.9%
50.56 918
 
1.8%
48.45 914
 
1.8%
Other values (115) 39741
77.8%
ValueCountFrequency (%)
45.94 114
 
0.2%
46.67 125
 
0.2%
47.265 165
 
0.3%
47.67 132
 
0.3%
47.935 215
 
0.4%
48.05 180
 
0.4%
48.09 360
0.7%
48.13 643
1.3%
48.165 424
0.8%
48.365 49
 
0.1%
ValueCountFrequency (%)
53.01 244
0.5%
52.85 524
1.0%
52.815 212
 
0.4%
52.79 289
0.6%
52.75 191
 
0.4%
52.74 338
0.7%
52.51 143
 
0.3%
52.45 294
0.6%
52.31666667 98
 
0.2%
52.28 586
1.1%

t2_champ5win
Real number (ℝ)

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.253677
Minimum45.94
Maximum53.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size797.7 KiB
2023-03-12T10:32:18.099162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum45.94
5-th percentile48.45
Q149.38
median50.09
Q351.04
95-th percentile52.28
Maximum53.01
Range7.07
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.1860175
Coefficient of variation (CV)0.023600611
Kurtosis-0.07542529
Mean50.253677
Median Absolute Deviation (MAD)0.81
Skewness0.10941222
Sum2565601
Variance1.4066375
MonotonicityNot monotonic
2023-03-12T10:32:18.361527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67 1651
 
3.2%
48.88 1291
 
2.5%
49.56 1253
 
2.5%
49.26 1237
 
2.4%
50.52 1099
 
2.2%
49.95 1065
 
2.1%
51.04 1030
 
2.0%
51.63 1022
 
2.0%
48.45 888
 
1.7%
50.56 841
 
1.6%
Other values (115) 39676
77.7%
ValueCountFrequency (%)
45.94 94
 
0.2%
46.67 154
 
0.3%
47.265 162
 
0.3%
47.67 107
 
0.2%
47.935 231
 
0.5%
48.05 165
 
0.3%
48.09 392
0.8%
48.13 620
1.2%
48.165 403
0.8%
48.365 47
 
0.1%
ValueCountFrequency (%)
53.01 262
0.5%
52.85 560
1.1%
52.815 222
 
0.4%
52.79 270
0.5%
52.75 183
 
0.4%
52.74 311
0.6%
52.51 145
 
0.3%
52.45 336
0.7%
52.31666667 103
 
0.2%
52.28 602
1.2%

firstBlood
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size797.7 KiB
1
25880 
2
24619 
0
 
554

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

Length

2023-03-12T10:32:18.600271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T10:32:18.821881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51053
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51053
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25880
50.7%
2 24619
48.2%
0 554
 
1.1%

firstTower
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size797.7 KiB
1
25630 
2
24218 
0
 
1205

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51053
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Length

2023-03-12T10:32:18.961089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T10:32:19.108048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51053
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 51053
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25630
50.2%
2 24218
47.4%
0 1205
 
2.4%

Interactions

2023-03-12T10:32:12.366706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:55.566351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.045237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:58.527414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.689970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.181429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:03.995168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:06.569669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:08.127435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:10.071116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:12.508541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:55.706522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.188999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:58.683701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.839052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.326932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:04.211027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:06.770928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:08.273487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:10.300386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:12.649312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:55.858284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.341986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:59.490879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.982737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.471752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:04.443325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:06.930713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:08.432791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:10.529068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.058784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.002518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.487391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:59.646481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.135010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.619035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:04.666042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.079873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:08.604066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:10.757826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.207428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.151414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.639013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:59.792064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.281672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.784221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:05.121207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.224497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:08.786865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:10.977648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.356816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.302170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.784115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:59.935807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.431891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.935424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:05.344862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.374326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:09.009440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:11.204398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.501354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.448916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:57.934915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.080189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.574530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:03.106994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:05.576731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.515386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:09.224542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:11.434240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.645510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.600157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:58.087105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.240620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.737697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:03.306672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:05.817303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.663193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:09.428657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:11.686045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.796993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.752442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:58.232472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.385244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:01.885979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:03.534179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:06.075868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.804151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:09.646227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:11.950958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:13.942218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:56.893368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:31:58.385872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:00.533192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:02.036566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:03.773984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:06.312534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:07.971227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:09.830295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-12T10:32:12.202302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-12T10:32:19.242608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTower
t1_champ1win1.000-0.017-0.000-0.015-0.011-0.010-0.011-0.0070.000-0.0120.0070.011
t2_champ1win-0.0171.000-0.011-0.005-0.016-0.011-0.020-0.013-0.007-0.0150.0080.013
t1_champ2win-0.000-0.0111.000-0.017-0.011-0.010-0.014-0.003-0.017-0.0110.0130.016
t2_champ2win-0.015-0.005-0.0171.000-0.007-0.003-0.010-0.0110.002-0.0130.0130.017
t1_champ3win-0.011-0.016-0.011-0.0071.000-0.010-0.009-0.010-0.002-0.0140.0000.018
t2_champ3win-0.010-0.011-0.010-0.003-0.0101.000-0.014-0.013-0.006-0.0120.0140.015
t1_champ4win-0.011-0.020-0.014-0.010-0.009-0.0141.000-0.0130.000-0.0060.0000.017
t2_champ4win-0.007-0.013-0.003-0.011-0.010-0.013-0.0131.000-0.018-0.0000.0070.015
t1_champ5win0.000-0.007-0.0170.002-0.002-0.0060.000-0.0181.000-0.0140.0110.016
t2_champ5win-0.012-0.015-0.011-0.013-0.014-0.012-0.006-0.000-0.0141.0000.0070.012
firstBlood0.0070.0080.0130.0130.0000.0140.0000.0070.0110.0071.0000.499
firstTower0.0110.0130.0160.0170.0180.0150.0170.0150.0160.0120.4991.000

Missing values

2023-03-12T10:32:14.141278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-12T10:32:14.373177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTower
050.35049.19000050.2350.6752.79050.37049.74000049.7550.45048.8821
151.32051.85000051.5250.0950.46551.55050.47000049.7549.88548.8812
249.95051.85000050.1050.7352.85048.09050.37000049.7549.74048.8812
347.93551.32000049.6650.6752.45049.74049.56000049.7549.92048.8822
449.26049.28000052.8548.4549.74049.86052.31666749.7550.95048.8821
549.92051.63000051.3249.7448.13049.11049.97500049.7548.85048.8821
649.97549.26000049.7450.4750.84051.71049.26000049.7550.65048.8811
748.45050.45000050.6752.1449.76049.27549.26000049.7549.11048.8821
850.52049.97500048.5851.3251.63049.38048.45000049.7545.94048.8810
949.95051.43666750.2350.0950.10050.18049.28333349.7549.46048.8821
t1_champ1wint2_champ1wint1_champ2wint2_champ2wint1_champ3wint2_champ3wint1_champ4wint2_champ4wint1_champ5wint2_champ5winfirstBloodfirstTower
5148049.3849.76052.75000050.0951.32049.35000050.84000051.83000050.42500050.4711
5148149.2650.67049.64500050.5249.92049.46666750.17000049.82000051.43666750.4712
5148250.8450.46549.64000050.9251.83051.87000050.52000049.82000051.32000050.4722
5148349.9548.96050.45000050.6749.97550.52000051.87000051.30000050.52000050.4712
5148452.2849.35049.83333351.8752.20051.04000051.03000049.11000050.10000050.4721
5148550.2350.84051.63000049.2850.53052.74000050.43000051.43666748.45000050.4721
5148650.5449.28049.26000052.8548.88049.62500048.67000048.16500050.83000050.4711
5148749.6850.52051.03000051.6348.16549.26000049.46666749.98500049.03000050.4711
5148850.3752.85049.28333349.2650.18048.16500051.32000050.95000050.23000050.4722
5148951.4350.92049.88500049.0349.28052.14000050.23000050.67000052.74000050.4711